WAYVision: A hybrid deep learning approach for recognizing handwritten Kannada Braille using wavelet transformation and attention based YOLOv5.

Handwritten Braille character recognition presents a significant challenge in the field of assistive technology, especially with the inclusion of various linguistic scripts such as Kannada. The data set is uniquely curated, combining ground-truth data from Kaggle and real-world samples collected from blind schools, segmented into vowels and consonants. The proposed system demonstrates exceptional performance in feature extraction, classification accuracy, and addressing spatial misalignments in Braille dots. Comparative analysis against state-of-the-art methods confirms the efficiency of the proposed model in overcoming the limitations of conventional techniques. The system was trained with two train test splits 70:30 and 80:20. The initial train test split has achieved 97.9 % and the latter one has achieved 98.7 %. This study aims to contribute significantly to the empowerment of visually impaired communities through advancements in automated Braille recognition systems.•The study addresses the challenge of handwritten Kannada Braille recognition using a uniquely curated dataset from Kaggle and blind schools, divided into vowels and consonants.•The proposed system achieves high accuracy (97.9 % for 70:30 and 98.7 % for 80:20 split) showing superior feature extraction and handling of spatial misalignments in Braille dots.•Comparative analysis of state-of-the-art methods confirms the model's efficiency in overcoming limitations of conventional techniques, contributing to assistive technology for visually impaired communities.